Bibliographic record
Abstract
Podemos hacer máquinas muy sensibles y las podríamos programar para que ellas se distingan a sí misma de otros objetos. Los programas que están concebidos hacia objetivos dados, como la identificación de objetos externos, también pueden ser imaginados como programas de acción que se refieren a la manipulación de estos objetos. Esos programas pueden ser concebidos para retener datos en el orden de su recepción, recoger patrones de aparición de perspectiva y anticipar sobre la base del éxito de sus actuaciones pasadas. De esta manera, podrían ser concebidos para permitir a la máquina identificar su propio aquí y ahora ; pero ¿Tendría un yo una máquina capaz de hacer esto? ¿Si así fuera, dónde estaría? El objeto de este artículo es abordar filosóficamente estas preguntas, revisando ampliamente la pregunta por el yo en la inteligencia artificial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".